





'''simple docstring'''






import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()






def   __A    (				a_    :	str   ,a_    :	str   ,a_    :	str   ,a_    :	PreTrainedTokenizer   ,a_    :	int   ,a_    :	Optional[int] = None   ,):
		lowerCAmelCase  :     Optional[Any]       						= {}

		if train_file is not None:
				lowerCAmelCase  :     Any       						= [train_file]
		if eval_file is not None:
				lowerCAmelCase  :     str       						= [eval_file]
		if test_file is not None:
				lowerCAmelCase  :     str       						= [test_file]

		lowerCAmelCase  :     Union[str, Any]       						= datasets.load_dataset("csv"   ,data_files=a_						)
		lowerCAmelCase  :     Dict       						= list(ds[list(files.keys()						)[0]].features.keys()						)
		lowerCAmelCase  :     Optional[int]       						= features_name.pop(a_						)
		lowerCAmelCase  :     Tuple       						= list(set(ds[list(files.keys()						)[0]][label_name]						)						)
		lowerCAmelCase  :     int       						= {label: i for i, label in enumerate(a_						)}
		lowerCAmelCase  :     Tuple       						= tokenizer.model_input_names
		lowerCAmelCase  :     str       						= {}

		if len(a_						) == 1:
				for k in files.keys():
						lowerCAmelCase  :     int       						= ds[k].map(
						    lambda a_						: tokenizer.batch_encode_plus(
						        example[features_name[0]]   ,truncation=a_   ,max_length=a_   ,padding="max_length"						)   ,batched=a_   ,)
		elif len(a_						) == 2:
				for k in files.keys():
						lowerCAmelCase  :     int       						= ds[k].map(
						    lambda a_						: tokenizer.batch_encode_plus(
						        (example[features_name[0]], example[features_name[1]])   ,truncation=a_   ,max_length=a_   ,padding="max_length"   ,)   ,batched=a_   ,)

		def gen_train():
				for ex in transformed_ds[datasets.Split.TRAIN]:
						lowerCAmelCase  :     List[Any]       						= {k: v for k, v in ex.items() if k in input_names}
						lowerCAmelCase  :     Optional[int]       						= labelaid[ex[label_name]]
						yield (d, label)

		def gen_val():
				for ex in transformed_ds[datasets.Split.VALIDATION]:
						lowerCAmelCase  :     Optional[int]       						= {k: v for k, v in ex.items() if k in input_names}
						lowerCAmelCase  :     Dict       						= labelaid[ex[label_name]]
						yield (d, label)

		def gen_test():
				for ex in transformed_ds[datasets.Split.TEST]:
						lowerCAmelCase  :     int       						= {k: v for k, v in ex.items() if k in input_names}
						lowerCAmelCase  :     Optional[Any]       						= labelaid[ex[label_name]]
						yield (d, label)

		lowerCAmelCase  :     Any       						= (
		    tf.data.Dataset.from_generator(
		        a_   ,({k: tf.intaa for k in input_names}, tf.intaa)   ,({k: tf.TensorShape([None]						) for k in input_names}, tf.TensorShape([]						))   ,)
		    if datasets.Split.TRAIN in transformed_ds
		    else None
		)

		if train_ds is not None:
				lowerCAmelCase  :     Union[str, Any]       						= train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]						)						)						)

		lowerCAmelCase  :     Dict       						= (
		    tf.data.Dataset.from_generator(
		        a_   ,({k: tf.intaa for k in input_names}, tf.intaa)   ,({k: tf.TensorShape([None]						) for k in input_names}, tf.TensorShape([]						))   ,)
		    if datasets.Split.VALIDATION in transformed_ds
		    else None
		)

		if val_ds is not None:
				lowerCAmelCase  :     List[Any]       						= val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]						)						)						)

		lowerCAmelCase  :     int       						= (
		    tf.data.Dataset.from_generator(
		        a_   ,({k: tf.intaa for k in input_names}, tf.intaa)   ,({k: tf.TensorShape([None]						) for k in input_names}, tf.TensorShape([]						))   ,)
		    if datasets.Split.TEST in transformed_ds
		    else None
		)

		if test_ds is not None:
				lowerCAmelCase  :     List[Any]       						= test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]						)						)						)

		return train_ds, val_ds, test_ds, labelaid


lowerCAmelCase									=   logging.getLogger(__name__)
@dataclass
class 		lowerCamelCase	:
	snake_case_   = field(metadata={"help": "Which column contains the label"}							)
	snake_case_   = field(default=_A   , metadata={"help": "The path of the training file"}							)
	snake_case_   = field(default=_A   , metadata={"help": "The path of the development file"}							)
	snake_case_   = field(default=_A   , metadata={"help": "The path of the test file"}							)
	snake_case_   = field(
	    default=128   , metadata={
	        "help": (
	            "The maximum total input sequence length after tokenization. Sequences longer "
	            "than this will be truncated, sequences shorter will be padded."
	        )
	    }   , )
	snake_case_   = field(
	    default=_A   , metadata={"help": "Overwrite the cached training and evaluation sets"}							)






@dataclass
class 		lowerCamelCase	:
	snake_case_   = field(
	    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}							)
	snake_case_   = field(
	    default=_A   , metadata={"help": "Pretrained config name or path if not the same as model_name"}							)
	snake_case_   = field(
	    default=_A   , metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}							)
	snake_case_   = field(default=_A   , metadata={"help": "Set this flag to use fast tokenization."}							)
	# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
	# or just modify its tokenizer_config.json.
	snake_case_   = field(
	    default=_A   , metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}   , )






def   __A    (				):
		# See all possible arguments in src/transformers/training_args.py
		# or by passing the --help flag to this script.
		# We now keep distinct sets of args, for a cleaner separation of concerns.
		lowerCAmelCase  :     Optional[int]       						= HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)						)
		lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  :     Tuple       						= parser.parse_args_into_dataclasses()

		if (
		    os.path.exists(training_args.output_dir						)
		    and os.listdir(training_args.output_dir						)
		    and training_args.do_train
		    and not training_args.overwrite_output_dir
		):
				raise ValueError(
				    f'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
				    " --overwrite_output_dir to overcome."						)

		# Setup logging
		logging.basicConfig(
		    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"   ,datefmt="%m/%d/%Y %H:%M:%S"   ,level=logging.INFO   ,)
		logger.info(
		    f'''n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1						)}, '''
		    f'''16-bits training: {training_args.fpaa}'''						)
		logger.info(f'''Training/evaluation parameters {training_args}'''						)

		# Load pretrained model and tokenizer
		#
		# Distributed training:
		# The .from_pretrained methods guarantee that only one local process can concurrently
		# download model & vocab.

		lowerCAmelCase  :     List[Any]       						= AutoTokenizer.from_pretrained(
		    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path   ,cache_dir=model_args.cache_dir   ,)

		lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  :     Optional[Any]       						= get_tfds(
		    train_file=data_args.train_file   ,eval_file=data_args.dev_file   ,test_file=data_args.test_file   ,tokenizer=a_   ,label_column_id=data_args.label_column_id   ,max_seq_length=data_args.max_seq_length   ,)

		lowerCAmelCase  :     List[Any]       						= AutoConfig.from_pretrained(
		    model_args.config_name if model_args.config_name else model_args.model_name_or_path   ,num_labels=len(a_						)   ,labelaid=a_   ,idalabel={id: label for label, id in labelaid.items()}   ,finetuning_task="text-classification"   ,cache_dir=model_args.cache_dir   ,)

		with training_args.strategy.scope():
				lowerCAmelCase  :     str       						= TFAutoModelForSequenceClassification.from_pretrained(
				    model_args.model_name_or_path   ,from_pt=bool(".bin" in model_args.model_name_or_path						)   ,config=a_   ,cache_dir=model_args.cache_dir   ,)

		def compute_metrics(a_    :	EvalPrediction						) -> Dict:
				lowerCAmelCase  :     Optional[Any]       						= np.argmax(p.predictions   ,axis=1						)

				return {"acc": (preds == p.label_ids).mean()}

		# Initialize our Trainer
		lowerCAmelCase  :     Union[str, Any]       						= TFTrainer(
		    model=a_   ,args=a_   ,train_dataset=a_   ,eval_dataset=a_   ,compute_metrics=a_   ,)

		# Training
		if training_args.do_train:
				trainer.train()
				trainer.save_model()
				tokenizer.save_pretrained(training_args.output_dir						)

		# Evaluation
		lowerCAmelCase  :     Tuple       						= {}
		if training_args.do_eval:
				logger.info("*** Evaluate ***"						)
				lowerCAmelCase  :     Optional[int]       						= trainer.evaluate()
				lowerCAmelCase  :     Optional[int]       						= os.path.join(training_args.output_dir   ,"eval_results.txt"						)

				with open(a_   ,"w"						) as writer:
						logger.info("***** Eval results *****"						)

						for key, value in result.items():
								logger.info(f'''  {key} = {value}'''						)
								writer.write(f'''{key} = {value}\n'''						)

						results.update(a_						)

		return results


if __name__ == "__main__":
					main()










'''simple docstring'''






lowerCAmelCase									=   [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_00_00)]






def   __A    (				a_    :	int						):
		lowerCAmelCase  :     Optional[int]       						= 0
		while number:
				# Increased Speed Slightly by checking every 5 digits together.
				sum_of_digits_squared += DIGITS_SQUARED[number % 1_0_0_0_0_0]
				number //= 1_0_0_0_0_0

		return sum_of_digits_squared


# There are 2 Chains made,
# One ends with 89 with the chain member 58 being the one which when declared first,
# there will be the least number of iterations for all the members to be checked.

# The other one ends with 1 and has only one element 1.

# So 58 and 1 are chosen to be declared at the starting.

# Changed dictionary to an array to quicken the solution
lowerCAmelCase									=   [None] * 10_00_00_00
lowerCAmelCase									=   True
lowerCAmelCase									=   False






def   __A    (				a_    :	int						):

		if CHAINS[number - 1] is not None:
				return CHAINS[number - 1]  # type: ignore

		lowerCAmelCase  :     Dict       						= chain(next_number(a_						)						)
		lowerCAmelCase  :     Union[str, Any]       						= number_chain

		while number < 1_0_0_0_0_0_0_0:
				lowerCAmelCase  :     Any       						= number_chain
				number *= 1_0

		return number_chain






def   __A    (				a_    :	int = 1_0_0_0_0_0_0_0						):
		for i in range(1   ,a_						):
				if CHAINS[i] is None:
						chain(i + 1						)

		return CHAINS[:number].count(a_						)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					print(F'''{solution() = }''')



